Sat.Feb 23, 2019 - Fri.Mar 01, 2019

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Managing Uber’s Data Workflows at Scale

Uber Engineering

At Uber’s scale, thousands of microservices serve millions of rides and deliveries a day, generating more than a hundred petabytes of raw data. Internally, engineering and data teams across the company leverage this data to improve the Uber experience. … The post Managing Uber’s Data Workflows at Scale appeared first on Uber Engineering Blog.

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All About the Kafka Connect Neo4j Sink Plugin

Confluent

Only a little more than one month after the first release, we are happy to announce another milestone for our Kafka integration. Today, you can grab the Kafka Connect Neo4j Sink from Confluent Hub. . Neo4j extension – Kafka sink refresher. We’ve been using the work we did for the Kafka sink – Neo4j extension and have made it available via remote connections over our binary bolt protocol.

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Cash Is Still King – Make Sure Your Business Is Prepared for the Next Recession

Teradata

If your organization understands customer profitability in detail, then your organization can easily navigate through a recession.

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How HelloFresh is Disrupting the Grocery Industry Using Deep Customer Insights.

Cloudera

We’ve just published our most recent customer success story ! This story gives a look at how HelloFresh is becoming a more data centric organization to better serve its customers. HelloFresh is the leading global provider of fresh ingredients and recipes that help families enjoy wholesome home-cooked meals with no planning or shopping. The company packages over 10 million meals a month for more than one and a half million customers worldwide.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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How to Build a Facebook Messenger Chatbot Powered by Fast SQL on CSV

Rockset

A chatbot, like any human customer service rep, needs data about your business and products in order to respond to customers with the correct information. What is an efficient way to hook up your data to a chat application without significant data engineering? In this blog, I will demonstrate how you can build a Facebook Messenger chatbot to help users find vacation rentals using CSV data on Airbnb rentals.

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How to Make Space for Research & Innovation?

Zalando Engineering

Redesigning research and product development so that the explorative nature of data science becomes a driver for innovation Zalando leverages cutting edge machine learning technologies to be Europe’s leading online platform for fashion and lifestyle. In order to develop these products, data scientists and product roles have to work together closely.

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Journey to Event Driven – Part 3: The Affinity Between Events, Streams and Serverless

Confluent

With serverless being all the rage, it brings with it a tidal change of innovation. Given that it is at a relatively early stage, developers are still trying to grok the best approach for each cloud vendor and often face the following question: Should I go cloud native with AWS Lambda, GCP functions, etc., or invest in a vendor-agnostic layer like the serverless framework ?

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Deep Learning For Data Engineers

Data Engineering Podcast

Summary Deep learning is the latest class of technology that is gaining widespread interest. As data engineers we are responsible for building and managing the platforms that power these models. To help us understand what is involved, we are joined this week by Thomas Henson. In this episode he shares his experiences experimenting with deep learning, what data engineers need to know about the infrastructure and data requirements to power the models that your team is building, and how it can be u

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Spring for Apache Kafka Deep Dive – Part 1: Error Handling, Message Conversion and Transaction Support

Confluent

Following on from How to Work with Apache Kafka in Your Spring Boot Application , which shows how to get started with Spring Boot and Apache Kafka ® , here we’ll dig a little deeper into some of the additional features that the Spring for Apache Kafka project provides. Spring for Apache Kafka brings the familiar Spring programming model to Kafka. It provides the KafkaTemplate for publishing records and a listener container for asynchronous execution of POJO listeners.

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